Ma304
Shashank
2022-04-29
Installing required libraries.
#Introduction Here our dataset contains the details about crimes in dallas, texas. The dataset contains various variables which contain the information about the officer, subject, crime and etc
df<-read.csv("C:/Users/User/37-00049_UOF-P_2016_prepped.csv")##preprocessing.
We choose dataframe and incident date and divide incident date column to month, year, date and removing duplicate row from the dataset.
df1 <- data.frame(df$INCIDENT_DATE)
df2 <-separate(df1, "df.INCIDENT_DATE", c("Month", "Date", "Year"), sep = "/")## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 1 rows [1].df2["Division"] <- df$DIVISION
df2=df2[-c(1), ]
df2## Month Date Year Division
## 2 9 3 16 CENTRAL
## 3 3 22 16 NORTHEAST
## 4 5 22 16 SOUTHWEST
## 5 1 10 16 NORTH CENTRAL
## 6 11 8 16 SOUTHEAST
## 7 9 11 16 NORTHEAST
## 8 11 26 16 CENTRAL
## 9 3 16 16 NORTHWEST
## 10 2 17 16 CENTRAL
## 11 12 24 16 NORTH CENTRAL
## 12 12 23 16 NORTHEAST
## 13 12 23 16 NORTHEAST
## 14 11 8 16 SOUTHEAST
## 15 10 23 16 NORTHEAST
## 16 10 23 16 NORTHEAST
## 17 10 18 16 NORTHEAST
## 18 10 12 16 NORTHEAST
## 19 10 10 16 CENTRAL
## 20 9 7 16 SOUTHEAST
## 21 10 8 16 NORTHEAST
## 22 10 8 16 NORTHEAST
## 23 10 3 16 SOUTHEAST
## 24 9 23 16 SOUTH CENTRAL
## 25 9 21 16 CENTRAL
## 26 9 13 16 NORTH CENTRAL
## 27 9 12 16 NORTH CENTRAL
## 28 8 3 16 NORTH CENTRAL
## 29 8 3 16 NORTH CENTRAL
## 30 8 3 16 NORTH CENTRAL
## 31 8 3 16 NORTH CENTRAL
## 32 7 28 16 SOUTHEAST
## 33 7 18 16 SOUTH CENTRAL
## 34 7 18 16 SOUTH CENTRAL
## 35 7 18 16 SOUTH CENTRAL
## 36 7 18 16 SOUTH CENTRAL
## 37 10 1 16 NORTH CENTRAL
## 38 7 18 16 SOUTH CENTRAL
## 39 7 18 16 SOUTHEAST
## 40 7 18 16 SOUTHEAST
## 41 7 18 16 SOUTHEAST
## 42 7 18 16 SOUTHEAST
## 43 7 18 16 SOUTHEAST
## 44 7 18 16 SOUTHEAST
## 45 7 18 16 SOUTHEAST
## 46 6 1 16 NORTH CENTRAL
## 47 6 1 16 NORTH CENTRAL
## 48 5 30 16 NORTHEAST
## 49 5 23 16 SOUTHEAST
## 50 5 23 16 SOUTHEAST
## 51 5 14 16 SOUTHEAST
## 52 4 16 16 SOUTHWEST
## 53 1 3 16 CENTRAL
## 54 4 3 16 SOUTHEAST
## 55 3 12 16 CENTRAL
## 56 3 12 16 CENTRAL
## 57 3 12 16 CENTRAL
## 58 3 12 16 CENTRAL
## 59 3 6 16 SOUTH CENTRAL
## 60 2 4 16 SOUTHEAST
## 61 2 4 16 SOUTHEAST
## 62 2 4 16 SOUTHEAST
## 63 2 4 16 NORTHWEST
## 64 1 10 16 NORTHWEST
## 65 1 8 16 SOUTHWEST
## 66 7 7 16 SOUTHWEST
## 67 11 20 16 SOUTHEAST
## 68 8 27 16 NORTHEAST
## 69 8 30 16 CENTRAL
## 70 9 11 16 CENTRAL
## 71 12 18 16 CENTRAL
## 72 10 29 16 SOUTHWEST
## 73 11 11 16 CENTRAL
## 74 11 13 16 CENTRAL
## 75 9 27 16 CENTRAL
## 76 7 10 16 CENTRAL
## 77 7 31 16 CENTRAL
## 78 8 19 16 NORTHWEST
## 79 12 1 16 NORTHEAST
## 80 12 16 16 SOUTH CENTRAL
## 81 12 9 16 SOUTH CENTRAL
## 82 7 23 16 CENTRAL
## 83 8 25 16 NORTHWEST
## 84 9 6 16 CENTRAL
## 85 9 27 16 CENTRAL
## 86 7 5 16 CENTRAL
## 87 9 11 16 CENTRAL
## 88 7 9 16 SOUTHWEST
## 89 9 7 16 SOUTHEAST
## 90 7 3 16 NORTH CENTRAL
## 91 12 2 16 NORTHEAST
## 92 7 14 16 CENTRAL
## 93 10 18 16 CENTRAL
## 94 11 2 16 NORTH CENTRAL
## 95 6 30 16 NORTHWEST
## 96 12 11 16 CENTRAL
## 97 9 28 16 NORTHWEST
## 98 8 31 16 NORTH CENTRAL
## 99 10 25 16 CENTRAL
## 100 11 11 16 CENTRAL
## 101 7 26 16 SOUTHEAST
## 102 11 17 16 SOUTHEAST
## 103 8 25 16 CENTRAL
## 104 8 18 16 SOUTHWEST
## 105 9 10 16 SOUTHEAST
## 106 8 10 16 NORTHWEST
## 107 8 31 16 NORTH CENTRAL
## 108 12 20 16 NORTHWEST
## 109 10 29 16 CENTRAL
## 110 9 3 16 NORTH CENTRAL
## 111 7 16 16 NORTHEAST
## 112 9 21 16 CENTRAL
## 113 10 2 16 SOUTH CENTRAL
## 114 6 30 16 NORTHWEST
## 115 7 15 16 SOUTHEAST
## 116 7 21 16 SOUTH CENTRAL
## 117 12 19 16 CENTRAL
## 118 7 16 16 CENTRAL
## 119 9 16 16 NORTH CENTRAL
## 120 8 28 16 NORTH CENTRAL
## 121 9 7 16 NORTH CENTRAL
## 122 11 19 16 SOUTH CENTRAL
## 123 10 1 16 NORTH CENTRAL
## 124 9 2 16 NORTHEAST
## 125 7 25 16 CENTRAL
## 126 11 25 16 SOUTH CENTRAL
## 127 12 2 16 CENTRAL
## 128 7 1 16 NORTHWEST
## 129 10 22 16 CENTRAL
## 130 9 13 16 CENTRAL
## 131 8 11 16 NORTH CENTRAL
## 132 9 3 16 NORTHEAST
## 133 7 18 16 NORTHWEST
## 134 10 18 16 SOUTHEAST
## 135 8 30 16 SOUTH CENTRAL
## 136 11 17 16 NORTH CENTRAL
## 137 10 6 16 NORTH CENTRAL
## 138 10 18 16 SOUTHEAST
## 139 10 12 16 CENTRAL
## 140 9 13 16 NORTH CENTRAL
## 141 7 16 16 NORTHEAST
## 142 9 3 16 SOUTHWEST
## 143 11 29 16 SOUTHEAST
## 144 9 4 16 CENTRAL
## 145 8 19 16 CENTRAL
## 146 11 25 16 NORTHEAST
## 147 8 29 16 CENTRAL
## 148 11 26 16 CENTRAL
## 149 10 11 16 SOUTHWEST
## 150 6 30 16 SOUTH CENTRAL
## 151 8 12 16 SOUTHEAST
## 152 11 26 16 CENTRAL
## 153 8 31 16 SOUTHEAST
## 154 7 14 16 SOUTHWEST
## 155 10 5 16 NORTHWEST
## 156 7 13 16 NORTHEAST
## 157 8 30 16 SOUTH CENTRAL
## 158 10 9 16 CENTRAL
## 159 8 8 16 CENTRAL
## 160 9 29 16 SOUTHWEST
## 161 7 24 16 NORTHWEST
## 162 10 16 16 SOUTH CENTRAL
## 163 7 13 16 NORTH CENTRAL
## 164 8 31 16 NORTHEAST
## 165 9 30 16 CENTRAL
## 166 9 28 16 CENTRAL
## 167 8 25 16 NORTHWEST
## 168 7 19 16 SOUTHEAST
## 169 12 27 16 SOUTHEAST
## 170 10 25 16 CENTRAL
## 171 9 11 16 CENTRAL
## 172 7 30 16 NORTHWEST
## 173 10 12 16 CENTRAL
## 174 8 30 16 CENTRAL
## 175 7 20 16 SOUTHEAST
## 176 10 9 16 NORTHEAST
## 177 11 22 16 SOUTHWEST
## 178 11 4 16 CENTRAL
## 179 11 26 16 CENTRAL
## 180 8 30 16 CENTRAL
## 181 7 16 16 CENTRAL
## 182 11 12 16 SOUTHWEST
## 183 10 19 16 NORTHEAST
## 184 7 8 16 CENTRAL
## 185 8 31 16 SOUTH CENTRAL
## 186 9 16 16 NORTHEAST
## 187 9 27 16 SOUTH CENTRAL
## 188 7 21 16 SOUTH CENTRAL
## 189 10 28 16 SOUTHWEST
## 190 11 3 16 NORTH CENTRAL
## 191 9 27 16 CENTRAL
## 192 10 6 16 NORTH CENTRAL
## 193 10 1 16 NORTH CENTRAL
## 194 9 6 16 SOUTHWEST
## 195 9 17 16 CENTRAL
## 196 8 11 16 NORTHEAST
## 197 12 28 16 NORTHEAST
## 198 7 2 16 NORTHWEST
## 199 12 12 16 NORTHEAST
## 200 12 1 16 SOUTHEAST
## 201 10 12 16 SOUTHEAST
## 202 10 28 16 CENTRAL
## 203 9 30 16 NORTHEAST
## 204 9 28 16 CENTRAL
## 205 9 25 16 NORTH CENTRAL
## 206 7 16 16 CENTRAL
## 207 8 16 16 NORTHEAST
## 208 8 12 16 SOUTHWEST
## 209 11 10 16 CENTRAL
## 210 10 31 16 CENTRAL
## 211 11 16 16 SOUTH CENTRAL
## 212 7 6 16 SOUTH CENTRAL
## 213 8 25 16 SOUTH CENTRAL
## 214 11 13 16 NORTHWEST
## 215 7 26 16 NORTH CENTRAL
## 216 10 17 16 CENTRAL
## 217 9 30 16 SOUTHEAST
## 218 11 27 16 SOUTHEAST
## 219 11 7 16 NORTHEAST
## 220 1 28 16 SOUTHEAST
## 221 7 2 16 NORTHWEST
## 222 12 29 16 NORTH CENTRAL
## 223 10 2 16 SOUTH CENTRAL
## 224 10 11 16 NORTHWEST
## 225 12 2 16 NORTHEAST
## 226 10 23 16 SOUTH CENTRAL
## 227 12 25 16 NORTH CENTRAL
## 228 10 11 16 SOUTHWEST
## 229 10 9 16 SOUTHWEST
## 230 7 11 16 CENTRAL
## 231 7 16 16 CENTRAL
## 232 8 11 16 NORTH CENTRAL
## 233 9 9 16 NORTHEAST
## 234 8 19 16 SOUTHWEST
## 235 11 24 16 NORTHEAST
## 236 9 15 16 CENTRAL
## 237 8 27 16 CENTRAL
## 238 11 26 16 NORTHEAST
## 239 9 11 16 NORTHEAST
## 240 8 28 16 SOUTHEAST
## 241 10 8 16 SOUTHWEST
## 242 7 16 16 CENTRAL
## 243 12 11 16 SOUTHEAST
## 244 12 19 16 CENTRAL
## 245 9 26 16 NORTHEAST
## 246 9 6 16 CENTRAL
## 247 10 20 16 NORTHEAST
## 248 10 27 16 CENTRAL
## 249 9 3 16 CENTRAL
## 250 11 23 16 SOUTH CENTRAL
## 251 7 3 16 NORTHWEST
## 252 12 19 16 NORTHWEST
## 253 7 24 16 CENTRAL
## 254 8 25 16 NORTH CENTRAL
## 255 9 21 16 CENTRAL
## 256 9 27 16 SOUTH CENTRAL
## 257 8 20 16 SOUTHEAST
## 258 12 10 16 SOUTHEAST
## 259 11 17 16 SOUTH CENTRAL
## 260 10 18 16 SOUTHEAST
## 261 12 1 16 SOUTHEAST
## 262 8 16 16 SOUTHEAST
## 263 10 22 16 SOUTHWEST
## 264 10 4 16 SOUTH CENTRAL
## 265 10 15 16 CENTRAL
## 266 9 10 16 SOUTHEAST
## 267 9 2 16 NORTHEAST
## 268 11 8 16 NORTHEAST
## 269 9 26 16 SOUTHWEST
## 270 7 5 16 CENTRAL
## 271 12 1 16 NORTHEAST
## 272 9 25 16 NORTHEAST
## 273 9 10 16 SOUTHWEST
## 274 10 5 16 SOUTH CENTRAL
## 275 12 8 16 NORTH CENTRAL
## 276 8 30 16 CENTRAL
## 277 9 10 16 SOUTHEAST
## 278 10 25 16 CENTRAL
## 279 9 27 16 CENTRAL
## 280 7 26 16 NORTH CENTRAL
## 281 7 9 16 SOUTHWEST
## 282 7 7 16 NORTHEAST
## 283 12 31 16 CENTRAL
## 284 9 14 16 NORTH CENTRAL
## 285 9 7 16 NORTH CENTRAL
## 286 7 9 16 SOUTH CENTRAL
## 287 9 18 16 CENTRAL
## 288 9 30 16 NORTH CENTRAL
## 289 12 20 16 NORTHEAST
## 290 7 30 16 NORTH CENTRAL
## 291 7 27 16 SOUTH CENTRAL
## 292 9 21 16 NORTHWEST
## 293 10 3 16 CENTRAL
## 294 10 31 16 CENTRAL
## 295 7 13 16 CENTRAL
## 296 11 5 16 NORTHEAST
## 297 11 26 16 NORTHWEST
## 298 7 5 16 CENTRAL
## 299 7 11 16 SOUTH CENTRAL
## 300 7 2 16 SOUTHWEST
## 301 8 9 16 SOUTHWEST
## 302 8 22 16 SOUTH CENTRAL
## 303 10 17 16 CENTRAL
## 304 9 30 16 SOUTHEAST
## 305 8 8 16 CENTRAL
## 306 12 10 16 SOUTH CENTRAL
## 307 10 16 16 NORTHWEST
## 308 8 25 16 NORTHWEST
## 309 8 17 16 NORTHEAST
## 310 9 26 16 NORTHEAST
## 311 7 11 16 CENTRAL
## 312 11 23 16 SOUTH CENTRAL
## 313 7 16 16 SOUTHEAST
## 314 7 25 16 NORTHEAST
## 315 8 17 16 NORTHEAST
## 316 8 28 16 NORTHWEST
## 317 12 9 16 CENTRAL
## 318 10 30 16 CENTRAL
## 319 10 9 16 NORTHEAST
## 320 11 27 16 NORTHEAST
## 321 8 8 16 SOUTHWEST
## 322 10 19 16 NORTH CENTRAL
## 323 10 18 16 SOUTHEAST
## 324 8 18 16 NORTHWEST
## 325 7 3 16 NORTHWEST
## 326 9 7 16 SOUTHEAST
## 327 11 26 16 NORTHEAST
## 328 8 5 16 NORTH CENTRAL
## 329 11 14 16 NORTH CENTRAL
## 330 8 9 16 SOUTHEAST
## 331 11 26 16 NORTHWEST
## 332 12 6 16 NORTH CENTRAL
## 333 12 17 16 CENTRAL
## 334 12 20 16 NORTHEAST
## 335 7 16 16 SOUTHEAST
## 336 7 9 16 SOUTHWEST
## 337 7 20 16 NORTH CENTRAL
## 338 9 28 16 CENTRAL
## 339 12 16 16 NORTHWEST
## 340 12 14 16 CENTRAL
## 341 7 16 16 SOUTHEAST
## 342 9 5 16 NORTH CENTRAL
## 343 9 25 16 NORTHEAST
## 344 9 30 16 SOUTHEAST
## 345 9 11 16 SOUTHEAST
## 346 8 22 16 SOUTHWEST
## 347 10 14 16 NORTHEAST
## 348 11 4 16 SOUTHWEST
## 349 10 25 16 CENTRAL
## 350 12 19 16 NORTH CENTRAL
## 351 12 25 16 NORTH CENTRAL
## 352 10 17 16 CENTRAL
## 353 9 23 16 SOUTH CENTRAL
## 354 10 18 16 CENTRAL
## 355 10 18 16 SOUTHEAST
## 356 8 31 16 SOUTH CENTRAL
## 357 9 27 16 NORTHEAST
## 358 12 26 16 CENTRAL
## 359 12 16 16 CENTRAL
## 360 8 14 16 SOUTH CENTRAL
## 361 10 21 16 SOUTH CENTRAL
## 362 12 3 16 SOUTHEAST
## 363 9 23 16 NORTHEAST
## 364 8 17 16 NORTHEAST
## 365 11 15 16 CENTRAL
## 366 8 6 16 SOUTHWEST
## 367 8 6 16 SOUTH CENTRAL
## 368 8 22 16 CENTRAL
## 369 7 5 16 NORTHEAST
## 370 11 12 16 SOUTHEAST
## 371 8 29 16 CENTRAL
## 372 7 25 16 NORTHEAST
## 373 9 18 16 NORTH CENTRAL
## 374 8 5 16 SOUTHEAST
## 375 7 28 16 SOUTHWEST
## 376 9 23 16 SOUTH CENTRAL
## 377 10 18 16 SOUTHEAST
## 378 11 13 16 NORTHEAST
## 379 10 3 16 CENTRAL
## 380 8 16 16 NORTHWEST
## 381 8 26 16 NORTHEAST
## 382 8 29 16 NORTHEAST
## 383 7 2 16 CENTRAL
## 384 11 12 16 NORTHEAST
## 385 11 20 16 CENTRAL
## 386 8 23 16 SOUTHEAST
## 387 9 10 16 NORTHEAST
## 388 9 14 16 SOUTHWEST
## 389 8 10 16 SOUTH CENTRAL
## 390 11 14 16 NORTHEAST
## 391 11 29 16 SOUTHEAST
## 392 12 19 16 CENTRAL
## 393 8 1 16 CENTRAL
## 394 8 23 16 NORTH CENTRAL
## 395 12 8 16 NORTH CENTRAL
## 396 10 27 16 CENTRAL
## 397 8 19 16 CENTRAL
## 398 11 27 16 SOUTHEAST
## 399 11 13 16 SOUTHEAST
## 400 8 17 16 CENTRAL
## 401 9 27 16 NORTHEAST
## 402 7 8 16 CENTRAL
## 403 12 7 16 SOUTH CENTRAL
## 404 8 19 16 SOUTH CENTRAL
## 405 11 17 16 SOUTH CENTRAL
## 406 11 1 16 SOUTHEAST
## 407 9 3 16 SOUTHEAST
## 408 8 17 16 NORTHEAST
## 409 8 6 16 CENTRAL
## 410 9 20 16 SOUTH CENTRAL
## 411 9 25 16 SOUTHEAST
## 412 8 21 16 SOUTHEAST
## 413 7 17 16 NORTHEAST
## 414 9 14 16 SOUTHWEST
## 415 7 20 16 CENTRAL
## 416 9 20 16 SOUTH CENTRAL
## 417 7 21 16 CENTRAL
## 418 11 13 16 NORTHWEST
## 419 11 27 16 CENTRAL
## 420 9 18 16 NORTH CENTRAL
## 421 11 19 16 NORTH CENTRAL
## 422 7 8 16 CENTRAL
## 423 8 27 16 SOUTHEAST
## 424 7 5 16 NORTHEAST
## 425 8 7 16 CENTRAL
## 426 12 7 16 NORTHEAST
## 427 10 18 16 SOUTHEAST
## 428 10 23 16 SOUTHWEST
## 429 11 12 16 NORTHWEST
## 430 11 27 16 CENTRAL
## 431 11 19 16 SOUTH CENTRAL
## 432 9 26 16 SOUTHWEST
## 433 8 19 16 SOUTHEAST
## 434 8 15 16 CENTRAL
## 435 10 19 16 NORTHEAST
## 436 7 18 16 SOUTH CENTRAL
## 437 7 3 16 NORTH CENTRAL
## 438 9 6 16 SOUTHWEST
## 439 12 3 16 SOUTHEAST
## 440 11 26 16 CENTRAL
## 441 9 2 16 NORTH CENTRAL
## 442 7 31 16 NORTH CENTRAL
## 443 8 19 16 SOUTH CENTRAL
## 444 11 17 16 SOUTH CENTRAL
## 445 9 28 16 CENTRAL
## 446 8 6 16 NORTH CENTRAL
## 447 12 1 16 SOUTHEAST
## 448 11 2 16 NORTH CENTRAL
## 449 10 2 16 NORTH CENTRAL
## 450 8 17 16 SOUTH CENTRAL
## 451 11 27 16 CENTRAL
## 452 9 25 16 NORTHEAST
## 453 9 17 16 CENTRAL
## 454 9 10 16 SOUTHWEST
## 455 7 10 16 NORTH CENTRAL
## 456 9 25 16 NORTH CENTRAL
## 457 9 11 16 SOUTHWEST
## 458 9 28 16 NORTHEAST
## 459 9 2 16 NORTHEAST
## 460 8 22 16 SOUTH CENTRAL
## 461 12 27 16 NORTHEAST
## 462 12 16 16 NORTH CENTRAL
## 463 9 17 16 NORTHEAST
## 464 9 2 16 NORTH CENTRAL
## 465 10 6 16 SOUTHWEST
## 466 9 19 16 SOUTHWEST
## 467 10 12 16 NORTHEAST
## 468 7 17 16 CENTRAL
## 469 7 1 16 NORTHEAST
## 470 11 3 16 NORTH CENTRAL
## 471 11 27 16 NORTHEAST
## 472 9 25 16 SOUTHWEST
## 473 7 17 16 SOUTH CENTRAL
## 474 9 15 16 CENTRAL
## 475 8 28 16 SOUTH CENTRAL
## 476 7 16 16 SOUTHWEST
## 477 8 15 16 SOUTHWEST
## 478 7 8 16 SOUTH CENTRAL
## 479 10 22 16 CENTRAL
## 480 7 14 16 CENTRAL
## 481 10 12 16 NORTHEAST
## 482 9 6 16 NORTH CENTRAL
## 483 8 2 16 NORTHEAST
## 484 7 18 16 NORTHWEST
## 485 10 1 16 NORTH CENTRAL
## 486 10 31 16 SOUTHEAST
## 487 8 7 16 CENTRAL
## 488 10 2 16 SOUTHWEST
## 489 11 17 16 NORTHWEST
## 490 9 11 16 NORTHWEST
## 491 8 4 16 CENTRAL
## 492 10 16 16 CENTRAL
## 493 7 5 16 SOUTH CENTRAL
## 494 8 24 16 SOUTHWEST
## 495 12 10 16 SOUTHEAST
## 496 9 5 16 NORTH CENTRAL
## 497 7 9 16 SOUTHWEST
## 498 8 28 16 NORTH CENTRAL
## 499 11 5 16 NORTHEAST
## 500 12 7 16 NORTHEAST
## 501 11 25 16 NORTHEAST
## 502 9 7 16 SOUTHEAST
## 503 9 24 16 NORTHWEST
## 504 10 30 16 NORTHWEST
## 505 8 27 16 NORTH CENTRAL
## 506 7 10 16 NORTH CENTRAL
## 507 10 22 16 CENTRAL
## 508 9 2 16 NORTHWEST
## 509 7 31 16 SOUTHWEST
## 510 9 30 16 NORTHEAST
## 511 8 15 16 CENTRAL
## 512 8 28 16 SOUTHEAST
## 513 7 31 16 NORTH CENTRAL
## 514 9 6 16 SOUTHWEST
## 515 8 28 16 SOUTHWEST
## 516 9 7 16 NORTH CENTRAL
## 517 8 31 16 SOUTH CENTRAL
## 518 8 10 16 NORTHWEST
## 519 10 9 16 NORTHEAST
## 520 7 6 16 CENTRAL
## 521 7 20 16 CENTRAL
## 522 9 26 16 NORTHWEST
## 523 11 5 16 NORTHEAST
## 524 8 9 16 SOUTHWEST
## 525 10 19 16 NORTHEAST
## 526 7 31 16 SOUTHEAST
## 527 11 21 16 SOUTHEAST
## 528 8 27 16 SOUTHWEST
## 529 8 21 16 SOUTHEAST
## 530 7 13 16 NORTHEAST
## 531 11 19 16 SOUTH CENTRAL
## 532 8 19 16 SOUTHEAST
## 533 10 17 16 CENTRAL
## 534 10 3 16 NORTHEAST
## 535 9 30 16 NORTH CENTRAL
## 536 10 6 16 NORTH CENTRAL
## 537 7 19 16 NORTH CENTRAL
## 538 8 18 16 SOUTH CENTRAL
## 539 12 16 16 CENTRAL
## 540 7 3 16 CENTRAL
## 541 8 13 16 SOUTHWEST
## 542 9 9 16 NORTHEAST
## 543 11 26 16 CENTRAL
## 544 7 19 16 SOUTHWEST
## 545 6 30 16 SOUTH CENTRAL
## 546 7 1 16 NORTHWEST
## 547 7 9 16 SOUTHWEST
## 548 7 9 16 SOUTHWEST
## 549 9 16 16 CENTRAL
## 550 10 9 16 SOUTHEAST
## 551 8 9 16 SOUTHWEST
## 552 8 25 16 CENTRAL
## 553 12 25 16 NORTH CENTRAL
## 554 8 11 16 CENTRAL
## 555 11 13 16 CENTRAL
## 556 12 13 16 CENTRAL
## 557 9 22 16 CENTRAL
## 558 9 27 16 CENTRAL
## 559 8 22 16 SOUTH CENTRAL
## 560 7 31 16 NORTH CENTRAL
## 561 9 21 16 SOUTHWEST
## 562 9 6 16 NORTH CENTRAL
## 563 7 8 16 NORTHEAST
## 564 10 16 16 NORTHWEST
## 565 11 20 16 CENTRAL
## 566 7 10 16 CENTRAL
## 567 8 20 16 SOUTHEAST
## 568 9 25 16 NORTH CENTRAL
## 569 9 21 16 CENTRAL
## 570 8 19 16 CENTRAL
## 571 9 10 16 NORTH CENTRAL
## 572 11 14 16 NORTH CENTRAL
## 573 11 12 16 NORTHWEST
## 574 7 5 16 SOUTH CENTRAL
## 575 11 20 16 CENTRAL
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## 577 7 24 16 CENTRAL
## 578 8 12 16 NORTH CENTRAL
## 579 8 31 16 SOUTHEAST
## 580 10 30 16 NORTHWEST
## 581 10 28 16 CENTRAL
## 582 9 6 16 CENTRAL
## 583 12 28 16 NORTHEAST
## 584 12 27 16 CENTRAL
## 585 7 18 16 SOUTHWEST
## 586 11 22 16 SOUTHWEST
## 587 9 10 16 SOUTHEAST
## 588 9 16 16 NORTHEAST
## 589 10 10 16 NORTHWEST
## 590 9 7 16 CENTRAL
## 591 8 28 16 SOUTHWEST
## 592 7 11 16 NORTH CENTRAL
## 593 8 2 16 NORTHEAST
## 594 8 28 16 SOUTH CENTRAL
## 595 7 11 16 SOUTH CENTRAL
## 596 8 4 16 SOUTH CENTRAL
## 597 10 23 16 CENTRAL
## 598 8 7 16 CENTRAL
## 599 8 15 16 CENTRAL
## 600 9 4 16 NORTH CENTRAL
## 601 8 26 16 SOUTH CENTRAL
## 602 9 12 16 NORTHWEST
## 603 7 8 16 NORTHEAST
## 604 12 18 16 CENTRAL
## 605 11 12 16 CENTRAL
## 606 8 22 16 CENTRAL
## 607 12 6 16 CENTRAL
## 608 8 17 16 NORTH CENTRAL
## 609 11 19 16 NORTHEAST
## 610 11 26 16 CENTRAL
## 611 8 6 16 SOUTH CENTRAL
## 612 7 1 16 SOUTHEAST
## 613 11 20 16 SOUTHEAST
## 614 7 23 16 CENTRAL
## 615 12 12 16 CENTRAL
## 616 11 21 16 NORTHWEST
## 617 12 20 16 NORTHWEST
## 618 10 18 16 NORTHWEST
## 619 12 8 16 NORTHWEST
## 620 9 2 16 NORTH CENTRAL
## 621 10 8 16 SOUTHEAST
## 622 7 2 16 SOUTHEAST
## 623 12 11 16 CENTRAL
## 624 7 1 16 SOUTHEAST
## 625 9 10 16 NORTH CENTRAL
## 626 7 8 16 SOUTHWEST
## 627 9 11 16 CENTRAL
## 628 7 15 16 SOUTHEAST
## 629 9 1 16 SOUTHWEST
## 630 7 16 16 SOUTHWEST
## 631 9 15 16 SOUTHEAST
## 632 9 11 16 NORTHEAST
## 633 8 28 16 SOUTH CENTRAL
## 634 8 6 16 CENTRAL
## 635 10 26 16 SOUTHEAST
## 636 11 21 16 NORTHEAST
## 637 10 16 16 SOUTH CENTRAL
## 638 11 26 16 SOUTH CENTRAL
## 639 11 2 16 NORTH CENTRAL
## 640 8 9 16 CENTRAL
## 641 9 28 16 CENTRAL
## 642 9 30 16 SOUTHEAST
## 643 8 10 16 SOUTHWEST
## 644 9 11 16 SOUTH CENTRAL
## 645 7 23 16 CENTRAL
## 646 10 1 16 NORTH CENTRAL
## 647 8 2 16 NORTHEAST
## 648 12 5 16 SOUTHEAST
## 649 10 23 16 SOUTHEAST
## 650 12 26 16 CENTRAL
## 651 7 2 16 SOUTHWEST
## 652 8 30 16 NORTHWEST
## 653 11 14 16 SOUTHEAST
## 654 11 1 16 NORTH CENTRAL
## 655 8 24 16 CENTRAL
## 656 9 3 16 SOUTHWEST
## 657 10 20 16 SOUTHEAST
## 658 10 25 16 CENTRAL
## 659 7 9 16 CENTRAL
## 660 9 11 16 NORTHEAST
## 661 7 26 16 NORTH CENTRAL
## 662 10 2 16 CENTRAL
## 663 5 17 16 SOUTH CENTRAL
## 664 9 30 16 SOUTHEAST
## 665 11 24 16 NORTHWEST
## 666 9 3 16 NORTHWEST
## 667 7 9 16 SOUTHWEST
## 668 12 8 16 CENTRAL
## 669 8 1 16 CENTRAL
## 670 11 29 16 SOUTHEAST
## 671 10 4 16 NORTH CENTRAL
## 672 9 26 16 NORTHWEST
## 673 6 30 16 NORTHWEST
## 674 12 20 16 NORTHWEST
## 675 10 1 16 NORTH CENTRAL
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## 725 9 17 16 NORTHEAST
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## 753 8 25 16 SOUTH CENTRAL
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## 880 7 10 16 NORTHEAST
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## 884 8 6 16 SOUTHWEST
## 885 10 25 16 SOUTHWEST
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## 904 11 26 16 NORTHEAST
## 905 11 18 16 NORTH CENTRAL
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## 907 10 23 16 SOUTH CENTRAL
## 908 11 19 16 NORTHEAST
## 909 8 17 16 NORTHEAST
## 910 11 29 16 SOUTHEAST
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## 919 12 23 16 NORTHEAST
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## 921 11 13 16 NORTHEAST
## 922 11 27 16 NORTH CENTRAL
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## 924 10 30 16 NORTHEAST
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## 948 12 26 16 NORTHEAST
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## 1314 4 20 16 SOUTH CENTRAL
## 1315 4 16 16 CENTRAL
## 1316 2 27 16 SOUTHEAST
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## 1958 6 10 16 NORTHEAST
## 1959 5 16 16 SOUTH CENTRAL
## 1960 2 28 16 SOUTH CENTRAL
## 1961 3 1 16 CENTRAL
## 1962 5 1 16 SOUTHWEST
## 1963 3 2 16 CENTRAL
## 1964 2 23 16 CENTRAL
## 1965 5 16 16 NORTHEAST
## 1966 5 10 16 NORTH CENTRAL
## 1967 3 30 16 SOUTH CENTRAL
## 1968 5 6 16 NORTHEAST
## 1969 3 15 16 SOUTH CENTRAL
## 1970 4 5 16 NORTH CENTRAL
## 1971 2 8 16 NORTH CENTRAL
## 1972 4 18 16 SOUTH CENTRAL
## 1973 3 3 16 CENTRAL
## 1974 3 21 16 NORTH CENTRAL
## 1975 3 14 16 SOUTHWEST
## 1976 3 27 16 CENTRAL
## 1977 6 19 16 SOUTHWEST
## 1978 4 6 16 SOUTH CENTRAL
## 1979 4 10 16 NORTH CENTRAL
## 1980 3 3 16 CENTRAL
## 1981 6 27 16 CENTRAL
## 1982 4 22 16 SOUTHEAST
## 1983 3 5 16 SOUTHEAST
## 1984 2 21 16 NORTH CENTRAL
## 1985 6 26 16 CENTRAL
## 1986 3 26 16 SOUTHEAST
## 1987 3 25 16 SOUTHWEST
## 1988 6 12 16 CENTRAL
## 1989 2 28 16 NORTHEAST
## 1990 3 1 16 SOUTH CENTRAL
## 1991 3 20 16 CENTRAL
## 1992 3 6 16 NORTHEAST
## 1993 3 30 16 SOUTHEAST
## 1994 6 24 16 NORTHEAST
## 1995 3 10 16 NORTHEAST
## 1996 3 8 16 NORTHEAST
## 1997 3 1 16 CENTRAL
## 1998 3 12 16 NORTHWEST
## 1999 2 26 16 NORTH CENTRAL
## 2000 6 13 16 SOUTHWEST
## 2001 6 2 16 NORTHWEST
## 2002 4 22 16 SOUTHEAST
## 2003 4 14 16 NORTHEAST
## 2004 4 2 16 CENTRAL
## 2005 3 1 16 NORTH CENTRAL
## 2006 5 7 16 CENTRAL
## 2007 2 18 16 NORTHEAST
## 2008 6 15 16 NORTHEAST
## 2009 6 20 16 CENTRAL
## 2010 3 23 16 CENTRAL
## 2011 3 5 16 CENTRAL
## 2012 2 27 16 NORTHWEST
## 2013 2 14 16 SOUTH CENTRAL
## 2014 3 27 16 NORTH CENTRAL
## 2015 2 23 16 SOUTHWEST
## 2016 1 31 16 CENTRAL
## 2017 2 3 16 NORTHEAST
## 2018 2 18 16 NORTH CENTRAL
## 2019 2 14 16 NORTHEAST
## 2020 2 6 16 SOUTHWEST
## 2021 1 31 16 SOUTHWEST
## 2022 2 17 16 SOUTHEAST
## 2023 1 26 16 CENTRAL
## 2024 1 17 16 SOUTHWEST
## 2025 2 12 16 NORTH CENTRAL
## 2026 1 9 16 SOUTHWEST
## 2027 1 9 16 SOUTHWEST
## 2028 2 11 16 SOUTH CENTRAL
## 2029 1 11 16 SOUTH CENTRAL
## 2030 1 4 16 NORTHWEST
## 2031 1 26 16 CENTRAL
## 2032 1 30 16 SOUTHWEST
## 2033 2 3 16 SOUTHWEST
## 2034 1 30 16 NORTHEAST
## 2035 2 6 16 SOUTHEAST
## 2036 1 8 16 CENTRAL
## 2037 1 27 16 NORTHWEST
## 2038 2 15 16 CENTRAL
## 2039 2 14 16 SOUTHWEST
## 2040 2 14 16 CENTRAL
## 2041 1 5 16 NORTH CENTRAL
## 2042 1 6 16 NORTHWEST
## 2043 1 24 16 CENTRAL
## 2044 1 31 16 SOUTHWEST
## 2045 1 28 16 NORTHEAST
## 2046 2 6 16 SOUTHWEST
## 2047 2 18 16 CENTRAL
## 2048 1 24 16 SOUTH CENTRAL
## 2049 1 6 16 SOUTH CENTRAL
## 2050 1 30 16 SOUTHWEST
## 2051 2 14 16 SOUTHWEST
## 2052 1 8 16 NORTHWEST
## 2053 2 15 16 NORTH CENTRAL
## 2054 1 8 16 CENTRAL
## 2055 1 16 16 NORTHEAST
## 2056 2 13 16 CENTRAL
## 2057 1 27 16 NORTH CENTRAL
## 2058 1 14 16 CENTRAL
## 2059 2 16 16 NORTH CENTRAL
## 2060 1 4 16 NORTHWEST
## 2061 2 8 16 SOUTHEAST
## 2062 1 18 16 SOUTHEAST
## 2063 1 5 16 NORTH CENTRAL
## 2064 2 15 16 NORTH CENTRAL
## 2065 2 4 16 SOUTHWEST
## 2066 1 18 16 NORTHEAST
## 2067 1 10 16 NORTHWEST
## 2068 1 25 16 SOUTHEAST
## 2069 1 22 16 SOUTHWEST
## 2070 1 18 16 NORTHWEST
## 2071 2 17 16 NORTHEAST
## 2072 1 30 16 SOUTHEAST
## 2073 1 22 16 CENTRAL
## 2074 1 11 16 NORTHEAST
## 2075 1 15 16 SOUTH CENTRAL
## 2076 2 13 16 SOUTHEAST
## 2077 2 3 16 SOUTHWEST
## 2078 2 6 16 CENTRAL
## 2079 2 6 16 SOUTH CENTRAL
## 2080 1 22 16 CENTRAL
## 2081 1 18 16 SOUTHEAST
## 2082 1 19 16 CENTRAL
## 2083 1 31 16 NORTH CENTRAL
## 2084 1 13 16 SOUTHEAST
## 2085 1 23 16 SOUTH CENTRAL
## 2086 2 3 16 NORTHEAST
## 2087 1 30 16 SOUTHEAST
## 2088 1 22 16 CENTRAL
## 2089 2 16 16 NORTH CENTRAL
## 2090 1 10 16 NORTHWEST
## 2091 2 7 16 SOUTHEAST
## 2092 2 14 16 CENTRAL
## 2093 2 6 16 SOUTH CENTRAL
## 2094 2 16 16 SOUTHWEST
## 2095 1 8 16 CENTRAL
## 2096 1 10 16 NORTHEAST
## 2097 2 6 16 SOUTH CENTRAL
## 2098 1 23 16 NORTHEAST
## 2099 2 3 16 NORTHEAST
## 2100 2 4 16 NORTHWEST
## 2101 1 14 16 NORTH CENTRAL
## 2102 1 29 16 CENTRAL
## 2103 1 22 16 SOUTHWEST
## 2104 2 13 16 SOUTHWEST
## 2105 1 2 16 NORTH CENTRAL
## 2106 1 5 16 NORTH CENTRAL
## 2107 2 14 16 CENTRAL
## 2108 2 4 16 SOUTHWEST
## 2109 1 11 16 NORTHEAST
## 2110 1 11 16 NORTHWEST
## 2111 1 13 16 NORTH CENTRAL
## 2112 2 7 16 SOUTHEAST
## 2113 1 8 16 SOUTHWEST
## 2114 2 8 16 NORTHWEST
## 2115 3 20 16 CENTRAL
## 2116 2 15 16 CENTRAL
## 2117 1 8 16 NORTHEAST
## 2118 1 31 16 SOUTHWEST
## 2119 1 19 16 SOUTHWEST
## 2120 2 13 16 SOUTHEAST
## 2121 1 24 16 SOUTHEAST
## 2122 1 17 16 SOUTHEAST
## 2123 1 10 16 SOUTHEAST
## 2124 1 11 16 NORTH CENTRAL
## 2125 2 1 16 SOUTHWEST
## 2126 1 8 16 CENTRAL
## 2127 2 3 16 SOUTHEAST
## 2128 2 6 16 SOUTH CENTRAL
## 2129 2 9 16 NORTH CENTRAL
## 2130 2 14 16 CENTRAL
## 2131 1 24 16 NORTH CENTRAL
## 2132 2 16 16 CENTRAL
## 2133 2 16 16 NORTH CENTRAL
## 2134 1 17 16 NORTH CENTRAL
## 2135 1 19 16 SOUTHWEST
## 2136 1 5 16 SOUTH CENTRAL
## 2137 1 14 16 CENTRAL
## 2138 2 17 16 CENTRAL
## 2139 1 16 16 NORTHEAST
## 2140 1 23 16 NORTHWEST
## 2141 1 2 16 SOUTH CENTRAL
## 2142 1 29 16 CENTRAL
## 2143 2 14 16 SOUTHEAST
## 2144 1 24 16 NORTHEAST
## 2145 2 14 16 SOUTHEAST
## 2146 1 17 16 SOUTHWEST
## 2147 1 14 16 SOUTHWEST
## 2148 2 13 16 CENTRAL
## 2149 1 15 16 NORTHWEST
## 2150 1 9 16 SOUTH CENTRAL
## 2151 2 16 16 CENTRAL
## 2152 1 24 16 NORTH CENTRAL
## 2153 2 5 16 SOUTHEAST
## 2154 2 9 16 NORTHWEST
## 2155 2 8 16 SOUTHEAST
## 2156 2 9 16 SOUTH CENTRAL
## 2157 1 1 16 CENTRAL
## 2158 2 17 16 NORTHEAST
## 2159 1 28 16 SOUTHEAST
## 2160 1 25 16 NORTH CENTRAL
## 2161 1 8 16 CENTRAL
## 2162 1 19 16 NORTHWEST
## 2163 1 8 16 CENTRAL
## 2164 1 20 16 SOUTH CENTRAL
## 2165 2 14 16 CENTRAL
## 2166 1 28 16 CENTRAL
## 2167 2 5 16 SOUTHEAST
## 2168 1 31 16 SOUTHWEST
## 2169 1 20 16 SOUTH CENTRAL
## 2170 1 11 16 SOUTH CENTRAL
## 2171 1 27 16 NORTH CENTRAL
## 2172 1 22 16 NORTHEAST
## 2173 2 3 16 NORTH CENTRAL
## 2174 1 8 16 CENTRAL
## 2175 1 23 16 SOUTH CENTRAL
## 2176 2 5 16 SOUTH CENTRAL
## 2177 2 18 16 CENTRAL
## 2178 1 9 16 SOUTHEAST
## 2179 1 8 16 SOUTHWEST
## 2180 2 17 16 SOUTHEAST
## 2181 1 22 16 NORTHEAST
## 2182 1 24 16 CENTRAL
## 2183 1 14 16 CENTRAL
## 2184 2 18 16 SOUTHWEST
## 2185 1 1 16 SOUTH CENTRAL
## 2186 1 7 16 CENTRAL
## 2187 1 31 16 NORTH CENTRAL
## 2188 1 16 16 NORTHEAST
## 2189 1 20 16 CENTRAL
## 2190 1 22 16 NORTHEAST
## 2191 1 30 16 SOUTHWEST
## 2192 1 1 16 SOUTHWEST
## 2193 1 22 16 SOUTHWEST
## 2194 1 8 16 NORTHWEST
## 2195 1 20 16 CENTRAL
## 2196 2 6 16 SOUTH CENTRAL
## 2197 1 22 16 SOUTHWEST
## 2198 2 8 16 SOUTH CENTRAL
## 2199 1 13 16 SOUTHWEST
## 2200 1 23 16 CENTRAL
## 2201 1 8 16 NORTHWEST
## 2202 1 29 16 CENTRAL
## 2203 2 11 16 SOUTH CENTRAL
## 2204 1 20 16 CENTRAL
## 2205 1 4 16 SOUTHEAST
## 2206 1 18 16 NORTHEAST
## 2207 1 10 16 CENTRAL
## 2208 2 13 16 CENTRAL
## 2209 2 14 16 SOUTHEAST
## 2210 1 2 16 CENTRAL
## 2211 1 31 16 NORTH CENTRAL
## 2212 1 27 16 NORTH CENTRAL
## 2213 2 5 16 SOUTHEAST
## 2214 2 15 16 CENTRAL
## 2215 1 30 16 SOUTHEAST
## 2216 1 20 16 NORTH CENTRAL
## 2217 1 13 16 SOUTHWEST
## 2218 2 4 16 SOUTHWEST
## 2219 2 2 16 SOUTHEAST
## 2220 1 28 16 CENTRAL
## 2221 2 15 16 CENTRAL
## 2222 2 13 16 NORTHWEST
## 2223 2 16 16 NORTH CENTRAL
## 2224 2 5 16 SOUTHEAST
## 2225 1 26 16 SOUTHEAST
## 2226 1 1 16 NORTH CENTRAL
## 2227 1 23 16 CENTRAL
## 2228 2 7 16 SOUTHEAST
## 2229 2 14 16 SOUTHEAST
## 2230 1 21 16 NORTHEAST
## 2231 1 30 16 SOUTHEAST
## 2232 1 17 16 SOUTHWEST
## 2233 1 11 16 SOUTH CENTRAL
## 2234 1 3 16 SOUTH CENTRAL
## 2235 2 14 16 NORTHWEST
## 2236 2 6 16 SOUTHWEST
## 2237 1 1 16 CENTRAL
## 2238 1 19 16 NORTHWEST
## 2239 2 16 16 NORTHEAST
## 2240 2 3 16 NORTHEAST
## 2241 4 9 16 CENTRAL
## 2242 2 14 16 SOUTHEAST
## 2243 1 29 16 NORTH CENTRAL
## 2244 1 15 16 NORTHWEST
## 2245 1 31 16 NORTH CENTRAL
## 2246 2 14 16 SOUTHEAST
## 2247 2 4 16 SOUTHWEST
## 2248 1 15 16 SOUTHEAST
## 2249 2 9 16 SOUTH CENTRAL
## 2250 1 28 16 SOUTHEAST
## 2251 2 13 16 NORTHEAST
## 2252 2 15 16 NORTHWEST
## 2253 1 25 16 SOUTHEAST
## 2254 1 24 16 NORTH CENTRAL
## 2255 2 6 16 SOUTHWEST
## 2256 1 2 16 SOUTHEAST
## 2257 2 14 16 SOUTHEAST
## 2258 2 8 16 SOUTH CENTRAL
## 2259 1 30 16 CENTRAL
## 2260 1 1 16 NORTHEAST
## 2261 1 31 16 SOUTHWEST
## 2262 2 17 16 NORTHEAST
## 2263 1 23 16 SOUTH CENTRAL
## 2264 1 13 16 CENTRAL
## 2265 2 12 16 SOUTH CENTRAL
## 2266 1 8 16 SOUTHWEST
## 2267 1 10 16 SOUTHEAST
## 2268 2 6 16 SOUTHWEST
## 2269 1 5 16 NORTH CENTRAL
## 2270 1 3 16 NORTHWEST
## 2271 2 5 16 SOUTHEAST
## 2272 2 17 16 SOUTHEAST
## 2273 1 7 16 SOUTH CENTRAL
## 2274 1 20 16 SOUTH CENTRAL
## 2275 2 6 16 SOUTH CENTRAL
## 2276 1 7 16 CENTRAL
## 2277 1 25 16 SOUTH CENTRAL
## 2278 2 13 16 CENTRAL
## 2279 1 30 16 NORTHEAST
## 2280 2 4 16 SOUTH CENTRAL
## 2281 2 6 16 SOUTHWEST
## 2282 1 23 16 NORTHWEST
## 2283 2 2 16 SOUTHWEST
## 2284 1 17 16 SOUTH CENTRAL
## 2285 1 13 16 NORTHEAST
## 2286 1 29 16 SOUTH CENTRAL
## 2287 1 28 16 SOUTHWEST
## 2288 4 6 16 SOUTHEAST
## 2289 2 6 16 SOUTHEAST
## 2290 1 12 16 CENTRAL
## 2291 2 14 16 SOUTHEAST
## 2292 1 10 16 NORTHWEST
## 2293 1 13 16 NORTHEAST
## 2294 1 30 16 SOUTHWEST
## 2295 3 7 16 SOUTHWEST
## 2296 1 15 16 SOUTH CENTRAL
## 2297 2 8 16 NORTHWEST
## 2298 1 7 16 SOUTH CENTRAL
## 2299 1 23 16 NORTHEAST
## 2300 2 9 16 SOUTH CENTRAL
## 2301 1 22 16 SOUTHWEST
## 2302 1 26 16 NORTH CENTRAL
## 2303 1 13 16 SOUTHEAST
## 2304 2 14 16 CENTRAL
## 2305 2 14 16 NORTH CENTRAL
## 2306 1 9 16 SOUTHWEST
## 2307 1 12 16 SOUTHEAST
## 2308 2 5 16 NORTHEAST
## 2309 2 17 16 CENTRAL
## 2310 1 20 16 SOUTH CENTRAL
## 2311 2 1 16 NORTHEAST
## 2312 1 31 16 SOUTHWEST
## 2313 1 11 16 NORTH CENTRAL
## 2314 2 12 16 NORTH CENTRAL
## 2315 2 13 16 SOUTHEAST
## 2316 2 5 16 NORTHEAST
## 2317 2 14 16 SOUTHWEST
## 2318 2 2 16 SOUTHEAST
## 2319 2 16 16 SOUTHWEST
## 2320 1 20 16 SOUTHEAST
## 2321 1 31 16 SOUTHWEST
## 2322 2 3 16 SOUTHWEST
## 2323 1 10 16 SOUTH CENTRAL
## 2324 2 12 16 CENTRAL
## 2325 2 6 16 SOUTHEAST
## 2326 1 21 16 SOUTHEAST
## 2327 1 28 16 NORTHWEST
## 2328 1 9 16 SOUTHEAST
## 2329 1 21 16 SOUTHEAST
## 2330 1 22 16 SOUTHWEST
## 2331 2 5 16 SOUTHEAST
## 2332 2 13 16 NORTHEAST
## 2333 1 5 16 SOUTHWEST
## 2334 1 9 16 SOUTH CENTRAL
## 2335 1 31 16 SOUTHEAST
## 2336 2 8 16 NORTHEAST
## 2337 1 23 16 CENTRAL
## 2338 1 28 16 NORTHEAST
## 2339 1 30 16 NORTHEAST
## 2340 1 15 16 NORTH CENTRAL
## 2341 1 3 16 NORTHWEST
## 2342 1 2 16 NORTHEAST
## 2343 2 3 16 SOUTHEAST
## 2344 1 4 16 SOUTHEAST
## 2345 2 5 16 NORTHEAST
## 2346 2 11 16 SOUTH CENTRAL
## 2347 2 16 16 NORTH CENTRAL
## 2348 2 11 16 CENTRAL
## 2349 2 15 16 NORTH CENTRAL
## 2350 2 14 16 CENTRAL
## 2351 1 9 16 SOUTH CENTRAL
## 2352 1 18 16 SOUTHEAST
## 2353 1 23 16 NORTHEAST
## 2354 1 9 16 SOUTH CENTRAL
## 2355 1 6 16 SOUTHWEST
## 2356 1 14 16 SOUTHWEST
## 2357 2 16 16 NORTHWEST
## 2358 1 5 16 SOUTHWEST
## 2359 1 9 16 CENTRAL
## 2360 2 2 16 SOUTHEAST
## 2361 2 10 16 NORTHEAST
## 2362 2 15 16 NORTH CENTRAL
## 2363 1 23 16 CENTRAL
## 2364 1 24 16 NORTH CENTRAL
## 2365 1 15 16 SOUTH CENTRAL
## 2366 1 15 16 SOUTH CENTRAL
## 2367 1 22 16 SOUTHEAST
## 2368 1 22 16 SOUTHWEST
## 2369 2 14 16 SOUTHEAST
## 2370 2 13 16 CENTRAL
## 2371 1 29 16 NORTH CENTRAL
## 2372 1 23 16 NORTH CENTRAL
## 2373 1 28 16 SOUTHWEST
## 2374 1 24 16 SOUTHEAST
## 2375 1 18 16 SOUTHEAST
## 2376 1 17 16 SOUTHEAST
## 2377 1 1 16 NORTHWEST
## 2378 2 12 16 CENTRAL
## 2379 1 22 16 SOUTHEAST
## 2380 1 3 16 NORTHWEST
## 2381 2 7 16 SOUTH CENTRAL
## 2382 1 28 16 NORTHWEST
## 2383 1 9 16 NORTHWEST
## 2384 1 6 16 SOUTH CENTRALWe check the dimension and see if we are having any null values and duplicate values. duplicate values are removed.
dim(df)## [1] 2384 47str(df)## 'data.frame': 2384 obs. of 47 variables:
## $ INCIDENT_DATE : chr "OCCURRED_D" "9/3/16" "3/22/16" "5/22/16" ...
## $ INCIDENT_TIME : chr "OCCURRED_T" "4:14:00 AM" "11:00:00 PM" "1:29:00 PM" ...
## $ UOF_NUMBER : chr "UOFNum" "37702" "33413" "34567" ...
## $ OFFICER_ID : chr "CURRENT_BA" "10810" "7706" "11014" ...
## $ OFFICER_GENDER : chr "OffSex" "Male" "Male" "Male" ...
## $ OFFICER_RACE : chr "OffRace" "Black" "White" "Black" ...
## $ OFFICER_HIRE_DATE : chr "HIRE_DT" "5/7/14" "1/8/99" "5/20/15" ...
## $ OFFICER_YEARS_ON_FORCE : chr "INCIDENT_DATE_LESS_" "2" "17" "1" ...
## $ OFFICER_INJURY : chr "OFF_INJURE" "No" "Yes" "No" ...
## $ OFFICER_INJURY_TYPE : chr "OFF_INJURE_DESC" "No injuries noted or visible" "Sprain/Strain" "No injuries noted or visible" ...
## $ OFFICER_HOSPITALIZATION : chr "OFF_HOSPIT" "No" "Yes" "No" ...
## $ SUBJECT_ID : chr "CitNum" "46424" "44324" "45126" ...
## $ SUBJECT_RACE : chr "CitRace" "Black" "Hispanic" "Hispanic" ...
## $ SUBJECT_GENDER : chr "CitSex" "Female" "Male" "Male" ...
## $ SUBJECT_INJURY : chr "CIT_INJURE" "Yes" "No" "No" ...
## $ SUBJECT_INJURY_TYPE : chr "SUBJ_INJURE_DESC" "Non-Visible Injury/Pain" "No injuries noted or visible" "No injuries noted or visible" ...
## $ SUBJECT_WAS_ARRESTED : chr "CIT_ARREST" "Yes" "Yes" "Yes" ...
## $ SUBJECT_DESCRIPTION : chr "CIT_INFL_A" "Mentally unstable" "Mentally unstable" "Unknown" ...
## $ SUBJECT_OFFENSE : chr "CitChargeT" "APOWW" "APOWW" "APOWW" ...
## $ REPORTING_AREA : chr "RA" "2062" "1197" "4153" ...
## $ BEAT : chr "BEAT" "134" "237" "432" ...
## $ SECTOR : chr "SECTOR" "130" "230" "430" ...
## $ DIVISION : chr "DIVISION" "CENTRAL" "NORTHEAST" "SOUTHWEST" ...
## $ LOCATION_DISTRICT : chr "DIST_NAME" "D14" "D9" "D6" ...
## $ STREET_NUMBER : chr "STREET_N" "211" "7647" "716" ...
## $ STREET_NAME : chr "STREET" "Ervay" "Ferguson" "bimebella dr" ...
## $ STREET_DIRECTION : chr "street_g" "N" "NULL" "NULL" ...
## $ STREET_TYPE : chr "street_t" "St." "Rd." "Ln." ...
## $ LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION: chr "Street Address" "211 N ERVAY ST" "7647 FERGUSON RD" "716 BIMEBELLA LN" ...
## $ LOCATION_CITY : chr "City" "Dallas" "Dallas" "Dallas" ...
## $ LOCATION_STATE : chr "State" "TX" "TX" "TX" ...
## $ LOCATION_LATITUDE : chr "Latitude" "32.782205" "32.798978" "32.73971" ...
## $ LOCATION_LONGITUDE : chr "Longitude" "-96.797461" "-96.717493" "-96.92519" ...
## $ INCIDENT_REASON : chr "SERVICE_TY" "Arrest" "Arrest" "Arrest" ...
## $ REASON_FOR_FORCE : chr "UOF_REASON" "Arrest" "Arrest" "Arrest" ...
## $ TYPE_OF_FORCE_USED1 : chr "ForceType1" "Hand/Arm/Elbow Strike" "Joint Locks" "Take Down - Group" ...
## $ TYPE_OF_FORCE_USED2 : chr "ForceType2" "" "" "" ...
## $ TYPE_OF_FORCE_USED3 : chr "ForceType3" "" "" "" ...
## $ TYPE_OF_FORCE_USED4 : chr "ForceType4" "" "" "" ...
## $ TYPE_OF_FORCE_USED5 : chr "ForceType5" "" "" "" ...
## $ TYPE_OF_FORCE_USED6 : chr "ForceType6" "" "" "" ...
## $ TYPE_OF_FORCE_USED7 : chr "ForceType7" "" "" "" ...
## $ TYPE_OF_FORCE_USED8 : chr "ForceType8" "" "" "" ...
## $ TYPE_OF_FORCE_USED9 : chr "ForceType9" "" "" "" ...
## $ TYPE_OF_FORCE_USED10 : chr "ForceType10" "" "" "" ...
## $ NUMBER_EC_CYCLES : chr "Cycles_Num" "NULL" "NULL" "NULL" ...
## $ FORCE_EFFECTIVE : chr "ForceEffec" " Yes" " Yes" " Yes" ...sum(is.na(df))## [1] 0duplicated(df)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [2377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEdf_new=df[-c(1), ]
View(df_new)Plot for officer race showing the count of the number of officers in each race.
ggplot(df_new, aes(x=OFFICER_RACE))+geom_bar()
Plot for officer gender which describe the gender of the officer.
ggplot(df_new, aes(x=OFFICER_GENDER))+geom_bar()
Plot for officer injury mentioning the details of injuries caused
ggplot(df_new, aes(x=OFFICER_INJURY))+geom_bar()
Plot Subject race detailing the race of the subject
ggplot(df_new, aes(x=SUBJECT_RACE))+geom_bar()
Here we can see the gender of the subject.
ggplot(df_new, aes(x=SUBJECT_GENDER))+geom_bar()
Count plot of Division.
ggplot(df_new, aes(x=DIVISION))+geom_bar()
Interactive plot of Month
ggplot(df2, aes(x=Month))+geom_bar()
ggplotly()Interactive plot of OFFICER_HOSPITALIZATION
ggplot(df_new, aes(x=OFFICER_HOSPITALIZATION))+geom_bar()
ggplotly()Plot for Subject_description
ggplot(df_new, aes(x=SUBJECT_DESCRIPTION))+geom_bar()
ggplotly()Setting our own theme
raquel_theme<-theme_classic()+
theme(axis.text = element_text(size=14),
axis.title = element_text(size=14),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
plot.title = element_text(size=18))Mapping libraries
library(shiny)
library(dplyr)
library(leaflet)Bar plot Between officer_race and officer_gender
ggplot(df_new, aes(x = OFFICER_RACE, fill = OFFICER_GENDER)) +
geom_bar(position = position_dodge(preserve = "single"))
Bar plot Between subject_race and subject_gender
ggplot(df_new, aes(x = SUBJECT_RACE, fill = SUBJECT_GENDER)) +
geom_bar(position = position_dodge(preserve = "single"))
GeomBar plot Between officer_race and officer_gender
g = ggplot(data = df_new, aes(x = OFFICER_RACE ))
g + geom_bar(aes(colour = OFFICER_GENDER))
GeomBar plot Between subject race and subject_gender.
g = ggplot(data = df_new, aes(x = SUBJECT_RACE ))
g + geom_bar(aes(colour = SUBJECT_GENDER))
table to fetch the list of offense
t1 = table(df_new$SUBJECT_OFFENSE)
t1 = sort(t1, decreasing = TRUE)t2 = round(100*table(df_new$SUBJECT_OFFENSE) / sum(table(df_new$SUBJECT_OFFENSE)), 5)
t2 = sort(t2, decreasing = TRUE)Merging the above two tables and converting into data frame
offense_proportion = data.frame(t1,t2)fetching the column names.
colnames(offense_proportion) ## [1] "Var1" "Freq" "Var1.1" "Freq.1"col_remove <- c('Var1.1')
offense_proportion <- offense_proportion %>%
select(- one_of(col_remove))renaming columns
colnames(offense_proportion)[colnames(offense_proportion) %in% c('Freq.1')] <- c('Percentage')
View(offense_proportion)t1 = sort(table(df_new$SUBJECT_RACE), decreasing = TRUE)
t2 = format(round(as.numeric(sort(100*table(df_new$SUBJECT_RACE) / sum(table(df_new$SUBJECT_RACE)), decreasing = TRUE),2)), nsmall = 2)t = data.frame(t1,t2)
View(t)To plot top10_offense(Decreasing order) and table for data frame conversion.
top10_offense = sort(table(df_new$SUBJECT_OFFENSE), decreasing = TRUE)[1:10]
top10_offense <- as.data.frame(top10_offense) Renaming the column names
colnames(top10_offense) <- c('Offense','Frequency')
top10_offense## Offense Frequency
## 1 APOWW 351
## 2 No Arrest 305
## 3 Public Intoxication 181
## 4 Warrant/Hold 110
## 5 Assault/FV 92
## 6 Assault/Public Servant 47
## 7 Evading Arrest 34
## 8 Public Intoxication, Resisting Arrest 31
## 9 Assault 29
## 10 Other Misdemeanor Arrest 29ggplot of offense and frequency
ggplot(top10_offense, aes(x = Offense,y = Frequency)) +
geom_bar(stat = "identity") +
geom_col(fill="#756bb1") +
theme_minimal()
Plot to analyse which officer(gender) are injured during execution.
ggplot(df_new, aes(x = OFFICER_GENDER, y=10, fill = OFFICER_INJURY)) +
geom_bar(stat = "identity", position = "fill")+
scale_y_continuous(labels = scales::percent) +
xlab("Officer gender") + ylab("Proportion") +
guides(fill=guide_legend(title="Officer injury")) +
ggtitle("Which officer(gender) are injured during execution") +
scale_fill_brewer(palette = "Greys")
Plot to analyse which officer(gender) has most injured the subjects
ggplot(df_new, aes(x = OFFICER_GENDER, y=10, fill = SUBJECT_INJURY)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
xlab("Officer gender") + ylab("Proportion") +
guides(fill=guide_legend(title="Subject injury")) +
ggtitle("which officer(gender) has most injured the subjects") +
scale_fill_brewer(palette = "Dark2")
Plot to visualize top 10 forces used by officers. Table to data frame conversion.
top_10_forces = sort(table(df_new$TYPE_OF_FORCE_USED1), decreasing = TRUE)[1:10]
View(top_10_forces)
top_forces <- as.data.frame(top_10_forces)
top_forces## Var1 Freq
## 1 Verbal Command 818
## 2 Weapon display at Person 329
## 3 Held Suspect Down 176
## 4 BD - Grabbed 154
## 5 Take Down - Arm 144
## 6 Joint Locks 140
## 7 Hand Controlled Escort 107
## 8 Taser 78
## 9 Taser Display at Person 69
## 10 Take Down - Body 68renaming column names.
colnames(top_forces) <- c('Forces','Frequency')bar plot between forces and frequency
ggplot(top_forces, aes(x = Forces, y = Frequency)) +
geom_bar(stat = "identity") +
geom_col(fill="#756bb1") +
theme_minimal()
lets analyse which are forces are mostly deployed on genders.
ggplot(data=subset(df_new, SUBJECT_GENDER!="Unknown" & SUBJECT_GENDER!="NULL" & (TYPE_OF_FORCE_USED1=="Verbal Command" | TYPE_OF_FORCE_USED1=="Weapon display at Person" | TYPE_OF_FORCE_USED1=="Held Suspect Down" | TYPE_OF_FORCE_USED1=="BD - Grabbed" | TYPE_OF_FORCE_USED1=="Take Down - Arm" | TYPE_OF_FORCE_USED1=="Taser")),
aes(x = SUBJECT_GENDER, y=10, fill = TYPE_OF_FORCE_USED1)) +
geom_bar(stat = "identity", position = "fill")+
scale_y_continuous(labels = scales::percent) +
xlab("Subject gender") + ylab("Proportion") +
guides(fill=guide_legend(title="Type of force used")) +
ggtitle("Type of force used on genders")
plot between subject race and type of force used.
ggplot(data=subset(df_new, SUBJECT_RACE!="NULL" & (TYPE_OF_FORCE_USED1=="Verbal Command" | TYPE_OF_FORCE_USED1=="Weapon display at Person" | TYPE_OF_FORCE_USED1=="Held Suspect Down" | TYPE_OF_FORCE_USED1=="BD - Grabbed" | TYPE_OF_FORCE_USED1=="Take Down - Arm" | TYPE_OF_FORCE_USED1=="Taser")), aes(x = SUBJECT_RACE, y=10, fill = TYPE_OF_FORCE_USED1)) +
geom_bar(stat = "identity", position = "fill")+
scale_y_continuous(labels = scales::percent) +
xlab("Subject race") + ylab("Proportion") +
guides(fill=guide_legend(title="Type of force"))+
ggtitle("Type of force used on races")
class(x = df_new$INCIDENT_DATE)## [1] "character"converting to date datatype
df_new$INCIDENT_DATE <- as.Date(df_new$INCIDENT_DATE, format = "%m/%d/%Y")
class(df_new$INCIDENT_DATE)## [1] "Date"fetching only the months from the date
df_new$INCIDENT_MONTH <- months(as.Date(df_new$INCIDENT_DATE))
df_new$INCIDENT_DATE <- as.Date(df_new$INCIDENT_DATE, format = "%Y-%m-%d")Counting the overall incidents from Jan’16 - Jan’17.
df_year <- df_new %>%
group_by(INCIDENT_DATE,INCIDENT_MONTH) %>%
summarise(count = n())## `summarise()` has grouped output by 'INCIDENT_DATE'. You can override using the
## `.groups` argument.View(df_year)plot the line graph for incident that took place in the period Jan’16-Jan’17.
ggplot(data = df_year, aes(INCIDENT_DATE, count)) +
geom_line(size=0.5, col="gray") +
geom_smooth(method = "loess", color = "green", span = 1/5) +
#theme_bw() +
labs(x="Months ", y= "No. of Incidents", title=" Incidents from Jan'16-Jan'17")## `geom_smooth()` using formula 'y ~ x'
df_new$INC_MONTH <-format(df_new$INCIDENT_DATE,"%m")Tabulating the overall incidents monthly wise.
df_month <- df_new %>%
group_by(INC_MONTH) %>%
summarise(count = n())
View(df_month)To visualize the incidents per month.
ggplot(df_month, aes(x=INC_MONTH, y =count, group = 2)) + geom_line() +
geom_line( size = 1,colour ="steelblue") +
labs(x="MONTHS", y= " COUNTS", title="Incidents per month") +
theme_bw()
Tabulating the incidents with regards to subject_race
subject_race <- df_new %>%
group_by(INC_MONTH,INCIDENT_MONTH,SUBJECT_RACE) %>%
summarise(count = n())## `summarise()` has grouped output by 'INC_MONTH', 'INCIDENT_MONTH'. You can
## override using the `.groups` argument.View(subject_race)Plot to visualize the incidents vs Subject_Race
ggplot() +
geom_line(data=subset(subject_race, SUBJECT_RACE=="Black" ) ,aes(y=count,x= INC_MONTH,colour="green",group=1),size=1 ) +
geom_line(data=subset(subject_race, SUBJECT_RACE=="Hispanic" ) ,aes(y=count,x= INC_MONTH,colour="red",group=1),size=1 ) +
geom_line(data=subset(subject_race, SUBJECT_RACE=="White" ) ,aes(y=count,x= INC_MONTH,colour="orange",group=1),size=1 ) +
#scale_color_discrete(name = "Y series", labels = c("BLACKS", "WHITES","HISPANICS")) +
labs(x="2016", y= "Counts", title=" Subject Race vs Incidents") +
scale_color_discrete(name = "Legend", labels = c("BLACKS", "WHITES","HISPANICS")) +
theme(axis.text.x=element_text( vjust=0.5),legend.position="bottom")
Tabulating the incidents with regards to division
division <- df_new %>%
group_by(INC_MONTH,DIVISION) %>%
summarise(count = n())## `summarise()` has grouped output by 'INC_MONTH'. You can override using the
## `.groups` argument.View(division)